INTEGRATING ARTIFICIAL INTELLIGENCE INTO CURRICULUM DELIVERY FOR EFFECTIVE CCMAS IMPLEMENTATION
Authors: Bassey Edet Okon, Grace Iniobong Effiong
DOI: 10.5281/zenodo.17339455
Published: July 2024
Abstract
<p><em>This study examines the Utilization of Artificial Intelligence (AI) Tools in the Content Delivery of CCMAS Programs in Nigerian Universities, employing a mixed-methods research design. The study integrates quantitative and qualitative approaches to explore the use of AI in delivering Comprehensive Curriculum and Management of Academic Systems (CCMAS). It is structured around five objectives, five research questions, and five hypotheses, focusing on the effectiveness, challenges, and perceptions of AI integration. A multi-stage sampling technique was adopted to select 1,200 university lecturers from the South-South geopolitical zone of Nigeria. Data collection utilized structured questionnaires featuring Likert-scale items to assess AI tool utilization, effectiveness, and perceptions. Descriptive statistics summarized demographic data and response patterns, while inferential techniques—correlation, regression, and chi-square analyses—examined relationships between AI usage, learning outcomes, and categorical factors like training and perceived effectiveness. Findings revealed significant progress in AI adoption for CCMAS content delivery, with 70% of lecturers actively using AI tools. Personalized learning enhanced student engagement and academic performance (r = 0.65, p < 0.01), while Intelligent Tutoring Systems improved academic outcomes by 15%. Challenges included insufficient training (45%), inadequate infrastructure (30%), and resistance to change (25%). However, 80% of respondents expressed positive perceptions of AI, indicating readiness for broader adoption. Hypothesis testing confirmed significant AI utilization (t (99) = 6.12, p < 0.001) and positive impacts of AI on learning outcomes. The study concludes that AI tools are being effectively utilized and positively perceived, but addressing barriers like training and infrastructure gaps is critical to maximizing their potential. It recommends targeted investments in training programs and infrastructure development to enhance educators' technical and pedagogical capacities for effective AI integration. </em></p>
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